510_FinalProject

library(limma)
library(Glimma)
library(edgeR)
library(dplyr)

library(magrittr)
setwd('~/Desktop/USC/Fall_2020/TRGN_510/510_adenocarcinoma_vs_squamous_cell_carcinoma')

Reading in count and metadata

# Reading in count data
count_matrix = read.table(file = 'count_processed.txt',sep = '\t',header = T,stringsAsFactors = F, check.names = F, row.names = 1)
count_matrix = count_matrix[,order(names(count_matrix))]
  

#Reading in clinical metadata
clinical_metadata = read.table(file = 'clinical_processed.txt', sep = '\t', header = T, as.is = T, check.names = F)
clinical_metadata = clinical_metadata[!duplicated(clinical_metadata[ , c("case_submitter_id")]),]

# Reading in exposure metadata
exposure_metadata = read.csv(file = 'exposure_processed.txt', sep = '\t', header = T, as.is = T, check.names = F)


# Merging metadata
all_metadata = merge(x = clinical_metadata, y = exposure_metadata, by = 'case_submitter_id')

all_metadata$ethnicity <- as.factor(all_metadata$ethnicity)
all_metadata$gender <- as.factor(all_metadata$gender)
all_metadata%<>%
  mutate(diagnosis=case_when(
    primary_diagnosis %in% c("Adenocarcinoma with mixed subtypes","Adenocarcinoma, NOS","Bronchiolo-alveolar adenocarcinoma, NOS","Papillary adenocarcinoma, NOS") ~ c("Adenocarcinoma"),
    primary_diagnosis %in% c("Squamous cell carcinoma, large cell, nonkeratinizing, NOS","Squamous cell carcinoma, NOS") ~ c("Squamous_cell_carcinoma")
  ))
all_metadata$diagnosis = as.factor(all_metadata$diagnosis)
all_metadata$race <- as.factor(all_metadata$race)
rownames(all_metadata) <- all_metadata$case_submitter_id
all_metadata = all_metadata[,-1]
all_metadata = all_metadata[order(rownames(all_metadata)),]

# Creating DGEList Object
geneExpr = DGEList(counts = count_matrix, samples = all_metadata)
geneExpr$samples$group=all_metadata$diagnosis
geneid = rownames(geneExpr)
# Importing gencode reference from DGC website 
gencode = read.table('gencode.gene.info.v22.tsv',sep = '\t',header = T,stringsAsFactors = F, check.names = F)
genes = gencode[geneid %in% gencode$gene_id, ]
genes = genes[,c("gene_id","gene_name","seqname")]
rownames(genes) = genes$gene_id
genes = genes[order(genes$gene_id),]
geneExpr$genes = genes[,-1]

Data pre-processing

Transformation from the raw-scale

cpm <- cpm(geneExpr)
lcpm <- cpm(geneExpr, log=TRUE)
L <- mean(geneExpr$samples$lib.size) * 1e-6
M <- median(geneExpr$samples$lib.size) * 1e-6
c(L,M)

[1] 52.92581 48.81745

The average library size for this dataset is about 61.1 million.

Removing genes that are lowly expressed

table(rowSums(geneExpr$counts==0)==75)

FALSE 
60483 

keep.exprs <- filterByExpr(geneExpr, group=geneExpr$samples$group)

Around 11% of genes in the dataset have zero counts across all 75 samples.

geneExpr <- geneExpr[keep.exprs,, keep.lib.sizes=FALSE]
dim(geneExpr)

[1] 21170    40

In this dataset, the median library size is 61.5 million and 10/61.5 is about 0.2, therefore the filterByExpr function keeps genes that have a CPM of 0.2 or more. With this cutoff the number of genes are reduced to 22200, about 36% of genes from what I started with.

lcpm.cutoff <- log2(10/M + 2/L)
library(RColorBrewer)
nsamples <- ncol(geneExpr)
col <- brewer.pal(nsamples, "Paired")
par(mfrow=c(1,2))
plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.55), las=2, main="", xlab="")
title(main="A. Raw data", xlab="Log-cpm")

abline(v=lcpm.cutoff, lty=3)
for (i in 2:nsamples){
den <- density(lcpm[,i])
lines(den$x, den$y, col=col[i], lwd=2)
}

legend("topright", colnames(geneExpr$counts), text.col=col, bty="n")
lcpm_filtered <- cpm(geneExpr, log=TRUE)
plot(density(lcpm_filtered[,1]), col=col[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="")

title(main="B. Filtered data", xlab="Log-cpm")
abline(v=lcpm.cutoff, lty=3)

for (i in 2:nsamples){
den <- density(lcpm_filtered[,i])
lines(den$x, den$y, col=col[i], lwd=2)
}
legend("topright", colnames(geneExpr$counts), text.col=col, bty="n")

This graph showed that before filtering the data, a large portion of genes within each samples are lowly-expressed with log-CPM values that are small or negative.

Normalising gene expression distributions

geneExpr = calcNormFactors(geneExpr, method = "TMM")
geneExpr$samples$norm.factors

 [1] 1.0391573 0.9244982 0.8856329 1.0521559 1.0145085 1.1196887 1.0727778 1.3600421 1.0171641 0.9386345 0.8890663 0.6747847
[13] 0.9553945 1.1161617 0.9624960 1.1869816 1.0062698 0.8520753 0.8883963 1.0043621 0.8191521 1.0129474 0.9248736 1.1260210
[25] 1.0694870 0.7173413 1.0284701 1.0027376 1.0325592 1.1019957 1.0554783 0.9856847 1.0846584 1.0126931 1.1959792 0.9179716
[37] 1.1264297 1.0857329 1.0489496 1.0082920

geneExpr_unnormalized <- geneExpr
geneExpr_unnormalized$samples$norm.factors <- 1
par(mfrow=c(1,2))
lcpm_unnormalized <- cpm(geneExpr_unnormalized, log=TRUE)
boxplot(lcpm_unnormalized, las=2, col=col, main="")
title(main="A. Example: Unnormalised data",ylab="Log-cpm")

geneExpr_normalized <- calcNormFactors(geneExpr_unnormalized)  
geneExpr_normalized$samples$norm.factors

 [1] 1.0391573 0.9244982 0.8856329 1.0521559 1.0145085 1.1196887 1.0727778 1.3600421 1.0171641 0.9386345 0.8890663 0.6747847
[13] 0.9553945 1.1161617 0.9624960 1.1869816 1.0062698 0.8520753 0.8883963 1.0043621 0.8191521 1.0129474 0.9248736 1.1260210
[25] 1.0694870 0.7173413 1.0284701 1.0027376 1.0325592 1.1019957 1.0554783 0.9856847 1.0846584 1.0126931 1.1959792 0.9179716
[37] 1.1264297 1.0857329 1.0489496 1.0082920

lcpm_normalized <- cpm(geneExpr_normalized, log=TRUE)
boxplot(lcpm_normalized, las=2, col=col, main="")
title(main="B. Example: Normalised data",ylab="Log-cpm")

This figure showed that after normalization the samples are more similar to each other.

Unsupervised clustering of samples

lcpm <- cpm(geneExpr, log=TRUE)
par(mfrow=c(1,2))
col.group <- geneExpr$samples$group
levels(col.group) <-  brewer.pal(nlevels(col.group), "Set1")

minimal value for n is 3, returning requested palette with 3 different levels

col.group <- as.character(col.group)

col.race <- geneExpr$samples$race
levels(col.race) <-  brewer.pal(nlevels(col.race), "Set2")
col.race <- as.character(col.race)

col.gender <- geneExpr$samples$gender
levels(col.gender) <-  brewer.pal(nlevels(col.gender), "Set3")

minimal value for n is 3, returning requested palette with 3 different levels

col.gender <- as.character(col.gender)

col.age <- geneExpr$samples$age_at_diagnosis
levels(col.age) <-  brewer.pal(nlevels(col.age), "Set1")

minimal value for n is 3, returning requested palette with 3 different levels

col.age <- as.character(col.age)

col.ethnicity <- geneExpr$samples$ethnicity
levels(col.ethnicity) <-  brewer.pal(nlevels(col.ethnicity), "Set2")

minimal value for n is 3, returning requested palette with 3 different levels

col.ethnicity <- as.character(col.ethnicity)

col.cigar_per_day <- geneExpr$samples$cigarettes_per_day
levels(col.cigar_per_day) <-  brewer.pal(nlevels(col.cigar_per_day), "Set3")

minimal value for n is 3, returning requested palette with 3 different levels

col.cigar_per_day <- as.character(col.cigar_per_day)

plotMDS(lcpm,labels=geneExpr$samples$group,  col=col.group)
title(main="A. Sample groups")

plotMDS(lcpm, labels=geneExpr$samples$gender, col=col.gender, dim=c(3,4))
title(main="B. Sex")

plotMDS(lcpm, labels=geneExpr$samples$age_at_diagnosis, col=col.age, dim=c(3,4))
title(main="C. Age at diagnosis")

plotMDS(lcpm, labels=geneExpr$samples$race, col=col.race, dim=c(3,4))
title(main="D. Race")

plotMDS(lcpm, labels=geneExpr$samples$ethnicity, col=col.ethnicity, dim=c(3,4))
title(main="E. Ethnicity")

plotMDS(lcpm, labels=geneExpr$samples$cigarettes_per_day, col=col.cigar_per_day, dim=c(3,4))
title(main="F. Cigarettes per day")

In the MDS plot, there is no separation between group of patients with early stage lung cancer and late stage lung cancer.

Differential expression analysis

Creating a design matrix and contrasts

diagnosis = geneExpr$samples$group
ethnicity = geneExpr$samples$ethnicity
sex = geneExpr$samples$gender
race = geneExpr$samples$race
age_at_diagnosis = geneExpr$samples$age_at_diagnosis
smoking_hist = geneExpr$samples$cigarettes_per_day

design = model.matrix(~0+diagnosis+sex)
colnames(design) <- gsub("diagnosis", "", colnames(design))
contr.matrix <- makeContrasts(
   AdenocarcinomavsSquamous_cell_carcinoma = Adenocarcinoma-Squamous_cell_carcinoma, 
   levels = colnames(design))
contr.matrix

                         Contrasts
Levels                    AdenocarcinomavsSquamous_cell_carcinoma
  Adenocarcinoma                                                1
  Squamous_cell_carcinoma                                      -1
  sexmale                                                       0

Removing heteroscedascity from count data

par(mfrow=c(1,2))
v <- voom(geneExpr, design, plot=TRUE)
v

An object of class "EList"
$genes

21165 more rows ...

$targets

35 more rows ...

$E
                   TCGA-22-4613 TCGA-22-5489 TCGA-22-5491 TCGA-33-AAS8 TCGA-34-5231 TCGA-43-7656 TCGA-43-8118 TCGA-44-5645
ENSG00000000003.13     6.578393     4.518445     7.050818     5.481583     6.016010     5.138312     5.436772     5.635659
ENSG00000000419.11     5.006541     5.125550     6.009261     5.535389     5.227853     6.273798     5.677880     4.093028
ENSG00000000457.12     3.899691     4.351443     4.164341     3.347290     4.310104     4.022563     3.425331     5.427188
ENSG00000000460.15     3.720341     4.789466     4.070052     3.040155     4.109311     3.920045     3.333184     4.295921
ENSG00000000938.11     4.311517     4.032854     2.267258     2.617683     2.708592     3.026093     5.302423     2.653449
                   TCGA-44-A47G TCGA-46-3765 TCGA-46-3766 TCGA-49-4486 TCGA-49-4487 TCGA-49-AARO TCGA-50-5942 TCGA-50-5946
ENSG00000000003.13     5.702467     6.113541     6.379925     6.279390     5.972862     6.359283     5.745927     5.480757
ENSG00000000419.11     4.281239     6.092018     5.071231     5.475511     5.916025     4.660764     4.466833     5.072669
ENSG00000000457.12     3.977077     3.866716     3.928589     4.975262     4.130680     3.932304     4.777173     4.218002
ENSG00000000460.15     2.528768     3.742487     2.508825     2.713849     4.464368     2.723976     1.971207     3.939701
ENSG00000000938.11     5.680948     3.611814     4.778872     2.166361     5.063277     5.362237     2.672767     1.804396
                   TCGA-50-8457 TCGA-50-8460 TCGA-55-7726 TCGA-55-8089 TCGA-56-5897 TCGA-56-A4ZJ TCGA-56-A5DR TCGA-63-A5MH
ENSG00000000003.13     5.061664     5.801764     5.618804     5.648883     4.869300     5.409876     5.176682     5.836250
ENSG00000000419.11     4.197116     4.935787     6.016039     5.237966     5.088500     5.088331     5.362201     5.024777
ENSG00000000457.12     4.293238     3.792598     3.814772     4.220320     3.935572     3.573813     4.258951     4.356796
ENSG00000000460.15     2.450756     1.987285     3.384872     3.679206     3.653911     2.581473     4.020476     4.482014
ENSG00000000938.11     4.286484     4.685892     3.749970     5.450806     4.716595     5.280737     2.910954     2.161463
                   TCGA-63-A5MY TCGA-64-1676 TCGA-68-8250 TCGA-73-4662 TCGA-77-A5G7 TCGA-85-8048 TCGA-86-7953 TCGA-86-8076
ENSG00000000003.13     5.890766     7.721230     6.362591     6.141768     5.962677     6.170662     5.978361     5.688321
ENSG00000000419.11     6.699551     6.669445     6.051110     4.699274     6.046526     5.186354     4.206129     4.600721
ENSG00000000457.12     4.139408     4.442916     3.510752     5.172006     4.208445     4.329645     3.843559     4.470898
ENSG00000000460.15     4.332709     3.469883     3.347400     4.412238     4.220873     4.220775     4.150741     2.488662
ENSG00000000938.11     2.534827     4.735432     4.758486     4.545843     2.619316     5.029770     4.653009     4.916381
                   TCGA-90-7766 TCGA-91-6828 TCGA-93-A4JO TCGA-96-7545 TCGA-98-A53C TCGA-99-AA5R TCGA-NJ-A4YQ TCGA-O1-A52J
ENSG00000000003.13     6.386395     5.854894     6.055768     6.086418     4.356866     4.898964     5.288840     5.802251
ENSG00000000419.11     5.411248     4.413630     5.220789     5.534683     4.524946     4.397344     4.731263     5.020307
ENSG00000000457.12     4.452007     4.641835     4.296636     3.867595     3.721552     4.155948     4.791851     4.284247
ENSG00000000460.15     3.854044     2.833918     3.301439     2.842824     2.332691     2.017264     3.217170     3.054791
ENSG00000000938.11     3.879929     4.778828     5.055971     4.323060     5.647853     6.152786     4.741985     5.868203
21165 more rows ...

$weights
          [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8]     [,9]    [,10]    [,11]    [,12]     [,13]
[1,] 1.9156382 2.265171 2.264350 2.213717 2.251193 2.259541 2.213335 2.134201 2.196382 2.178912 2.103449 2.260762 1.9845015
[2,] 1.7274124 2.263898 2.259424 2.130467 2.259430 2.249206 2.129639 1.709450 1.843064 2.068303 1.956363 2.159220 1.4994892
[3,] 1.1247909 1.889080 1.831902 1.519393 2.058846 1.750188 1.518138 1.492255 1.626831 1.428855 1.303977 1.918998 1.3029032
[4,] 0.9981961 1.764861 1.705691 1.323075 1.957294 1.621630 1.321983 1.007585 1.085743 1.245279 1.141339 1.377043 0.9075725
[5,] 1.2599568 1.638856 1.578728 1.704841 1.839755 1.497199 1.703511 1.700961 1.835126 1.608547 1.469780 1.696029 1.4916402
        [,14]    [,15]    [,16]    [,17]    [,18]    [,19]    [,20]    [,21]    [,22]    [,23]    [,24]    [,25]     [,26]
[1,] 2.199452 2.147720 2.261012 2.239719 2.192143 1.912564 2.265181 2.254155 2.229398 2.217851 2.221969 2.247060 2.0726890
[2,] 1.850504 1.736032 2.236428 1.982529 1.936786 1.422262 2.210388 2.240132 2.157465 2.188616 2.194131 2.229313 1.7227708
[3,] 1.634603 1.518237 2.096214 1.780240 1.608988 1.237064 2.026705 1.700806 1.572091 1.526228 1.539838 1.649835 1.3955026
[4,] 1.090570 1.022096 1.590228 1.189021 1.130158 0.874648 1.494592 1.571770 1.369460 1.405102 1.417681 1.522205 0.9983615
[5,] 1.842526 1.728006 1.913948 1.975738 1.388865 1.414733 1.821489 1.450037 1.757968 1.295786 1.307260 1.403482 1.2056881
        [,27]    [,28]    [,29]    [,30]    [,31]    [,32]    [,33]     [,34]    [,35]    [,36]    [,37]    [,38]    [,39]
[1,] 2.238861 2.258105 2.162523 2.207645 2.263954 2.264046 2.260432 2.0272364 2.252693 2.259175 2.235016 2.216680 2.171008
[2,] 2.217553 2.217851 2.116843 2.174970 2.125441 2.182022 2.229632 1.6608334 2.118335 2.248536 2.170787 1.901291 1.781816
[3,] 1.609251 2.124609 1.389669 1.493012 1.965746 1.964307 1.768653 1.3415540 1.847571 1.745479 1.598156 1.688464 1.563408
[4,] 1.483421 1.549154 1.279861 1.374249 1.347076 1.422712 1.554026 0.9676036 1.311413 1.616765 1.392875 1.125273 1.047906
[5,] 1.367369 2.215406 1.183066 1.267782 2.120177 1.746381 1.945727 1.1615881 1.619536 1.492648 1.784314 1.893762 1.773524
        [,40]
[1,] 2.221055
[2,] 1.914839
[3,] 1.703532
[4,] 1.134973
[5,] 1.907243
21165 more rows ...

$design
  Adenocarcinoma Squamous_cell_carcinoma sexmale
1              0                       1       0
2              0                       1       1
3              0                       1       1
4              0                       1       0
5              0                       1       1
35 more rows ...

vfit <- lmFit(v, design)
vfit <- contrasts.fit(vfit, contrasts=contr.matrix)
efit <- eBayes(vfit)
plotSA(efit, main="Final model: Mean-variance trend")

Examining the number of DE genes

summary(decideTests(efit))

       AdenocarcinomavsSquamous_cell_carcinoma
Down                                      2482
NotSig                                   16700
Up                                        1988

res = topTable(efit,sort.by = "P",n=Inf)
head(res)

DEGs with LFC greater than 1

tfit <- treat(vfit, lfc=1)
dt <- decideTests(tfit)
summary(dt)

       AdenocarcinomavsSquamous_cell_carcinoma
Down                                       320
NotSig                                   20769
Up                                          81

Useful graphical representations of differential expression results

MD plot with Up & Down regulated DEGs

plotMD(tfit, column=1, status=dt[,1], main=colnames(tfit)[1], 
       xlim=c(-8,13))

glMDPlot(tfit, coef=1, status=dt, main=colnames(tfit)[1],
         side.main="gene_name", counts=lcpm, groups=geneExpr$samples$group, launch=FALSE)

Heatmap with top 100 genes

library(gplots)
par(mar = rep(2, 4))
Adenocarcinoma.vs.SquamousCell.topgenes <- res$gene_name[1:100]
i <- which(v$genes$gene_name %in% Adenocarcinoma.vs.SquamousCell.topgenes)
mycol <- colorpanel(1000,"blue","white","red")
heatmap.2(lcpm[i,], scale="row",
   labRow=v$genes$gene_name[i], labCol=v$targets$group, 
   col=mycol, trace="none", density.info="none", 
   margin=c(8,12), lhei=c(2,10), dendrogram="column")

Gene set testing with camera

load("human_c2_v5p2.rdata")
#idx <- ids2indices(Hs.c6,id=v$genes$gene_name)
library(Homo.sapiens)